Ibs sensitivity testing

ABSTRACT

Contemplated test kits and methods for food sensitivity are based on rational-based selection of food preparations with established discriminatory p-value. Particularly preferred kits include those with a minimum number of food preparations that have an average discriminatory p-value of ≤0.07 as determined by their raw p-value or an average discriminatory p-value of ≤0.10 as determined by FDR multiplicity adjusted p-value. In further contemplated aspects, compositions and methods for food sensitivity are also stratified by gender to further enhance predictive value.

This application claims priority to our U.S. provisional patent application with the Ser. No. 62/079,783 filed Nov. 14, 2014 which is incorporated by reference herein in its entirety.

FIELD OF THE INVENTION

The field of the invention is sensitivity testing for food intolerance, and especially as it relates to testing and possible elimination of selected food items as trigger foods for patients diagnosed with or suspected to have irritable bowel syndrome.

BACKGROUND

The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.

Food sensitivity, especially as it relates to irritable bowel syndrome (IBS), often presents with chronic abdominal pain, discomfort, bloating, and/or change in bowel habits and is not well understood in the medical community. Most typically, IBS is diagnosed by elimination of other pathological conditions (e.g., bacterial or protozoan infection, lactose intolerance, etc.) that may have similar or overlapping symptoms. However, IBS is often quite diverse with respect to dietary items triggering symptoms, and no standardized test to help identify trigger food items with a reasonable degree of certainty is known, leaving such patients often to trial-and-error.

While there are some commercially available tests and labs to help identify trigger foods, the quality of the test results from these labs is generally poor as is reported by a consumer advocacy group (e.g., http://www.which.co.uk/news/2008/08/food-allergy-tests-could-risk-your-health-154711/). Most notably, problems associated with these tests and labs were high false positive rates, high false negative rates, high intra-patient variability, and inter-laboratory variability, rendering such tests nearly useless. Similarly, further inconclusive and highly variable test results were also reported elsewhere (Alternative Medicine Review, Vol. 9, No. 2, 2004: pp 198-207), and the authors concluded that this may be due to food reactions and food sensitivities occurring via a number of different mechanisms. For example, not all IBS patients show positive response to food A, and not all IBS patients show negative response to food B. Thus, even if an IBS patient shows positive response to food A, removal of food A from the patient's diet may not relieve the patient's IBS symptoms. In other words, it is not well determined whether food samples used in the currently available tests are properly selected based on the high probabilities to correlate sensitivities to those food samples to IBS.

Many have made efforts to select food items or allergens to include in the test panel for immunoassay tests. For example, US Patent Application No. 2007/0122840 to Cousins discloses selection of 29 food allergens that are included in the test panel for ELISA assay. The 29 food allergens are selected based on the frequency of IgG positivity in preliminary experiments with a larger panel of food allergens. However, Cousins fails to teach any quantitative and/or statistical analysis for the selected antigens and as such fails to provide any rationale for the selection. Indeed, Cousin's method to select 29 food allergens for test panel has been criticized that the selection is rather arbitrary. For example, Croft criticized in a paper titled “IgG food antibodies and irritating the bowel”, published in Gastroenterology, Vol. 128, Issue 4, p. 1135-1136, that Cousin's method is not clear whether the quantity and range of food antibodies being measured are similar or completely different to non-IBS patients or non-food intolerant patients because it lacks controls (normal or non-IBS control subject). Thus, it is at best unclear if Cousins achieves any improvement with respect to false positive and false negative results.

For another example, US Patent Application No. 2011/0306898 to Stierstorfer discloses selection of 41 food substances as test materials on skin patches. The 41 food substances are selected based on chemical compounds included in the food substances (e.g., vanillin, cinnamic aldehyde, sorbic acid, etc.). The food substances are tested on IBS patients or IBS-suspected patients for allergic contact dermatitis. However, Stierstorfer also fails to disclose how the false positive or false negative food allergens are eliminated and whether the food allergens are selected based on the gender stratification among IgG positivity results.

All publications identified herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.

Thus, even though various tests for food sensitivities are known in the art, all or almost all of them suffer from one or more disadvantages. Therefore, there is still a need for improved compositions, devices, and methods of food sensitivity testing, especially for identification and possible elimination of trigger foods for patients identified with or suspected of having IBS.

SUMMARY OF THE INVENTION

The inventive subject matter provides systems and methods for testing food intolerance in patients diagnosed with or suspected to have irritable bowel syndrome. One aspect of the invention is a test kit with for testing food intolerance in patients diagnosed with or suspected to have irritable bowel syndrome. The test kit includes a plurality of distinct food preparations coupled to individually addressable respective solid carriers. The plurality of distinct food preparations have an average discriminatory p-value of ≤0.07 as determined by raw p-value or an average discriminatory p-value of ≤0.10 as determined by FDR multiplicity adjusted p-value.

Another aspect of the invention includes a method of testing food intolerance in patients diagnosed with or suspected to have irritable bowel syndrome. The method includes a step of contacting a food preparation with a bodily fluid of a patient that is diagnosed with or suspected to have irritable bowel syndrome. The bodily fluid is associated with a gender identification. It is especially preferred that the step of contacting is performed under conditions that allow IgG from the bodily fluid to bind to at least one component of the food preparation. The method continues with a step of measuring IgG bound to the at least one component of the food preparation to obtain a signal, and then comparing the signal to a gender-stratified reference value for the food preparation using the gender identification to obtain a result. Then, the method also includes a step of updating or generating a report using the result.

Another aspect of the invention includes a method of generating a test for food intolerance in patients diagnosed with or suspected to have irritable bowel syndrome. The method includes a step of obtaining test results for a plurality of distinct food preparations. The test results are based on bodily fluids of patients diagnosed with or suspected to have irritable bowel syndrome and bodily fluids of a control group not diagnosed with or not suspected to have irritable bowel syndrome. The method also includes a step of stratifying the test results by gender for each of the distinct food preparations. Then the method continues with a step of assigning for a predetermined percentile rank a different cutoff value for male and female patients for each of the distinct food preparations.

Still another aspect of the invention includes a use of a plurality of distinct food preparations coupled to individually addressable respective solid carriers in a diagnosis of irritable bowel syndrome. The plurality of distinct food preparations are selected based on their average discriminatory p-value of ≤0.07 as determined by raw p-value or an average discriminatory p-value of ≤0.10 as determined by FDR multiplicity adjusted p-value.

Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS

Table 1 shows a list of food items from which food preparations can be prepared.

Table 2 shows statistical data of foods ranked according to 2-tailed FDR multiplicity-adjusted p-values.

Table 3 shows statistical data of ELISA score by food and gender.

Table 4 shows cutoff values of foods for a predetermined percentile rank.

FIG. 1A illustrates ELISA signal score of male IBS patients and control tested with white wheat.

FIG. 1B illustrates a distribution of percentage of male IBS subjects exceeding the 90^(th) and 95^(th) percentile tested with white wheat.

FIG. 1C illustrates a signal distribution in women along with the 95^(th) percentile cutoff as determined from the female control population tested with white wheat.

FIG. 1D illustrates a distribution of percentage of female IBS subjects exceeding the 90^(th) and 95^(th) percentile tested with white wheat.

FIG. 2A illustrates ELISA signal score of male IBS patients and control tested with cocoa.

FIG. 2B illustrates a distribution of percentage of male IBS subjects exceeding the 90^(th) and 95^(th) percentile tested with cocoa.

FIG. 2C illustrates a signal distribution in women along with the 95^(th) percentile cutoff as determined from the female control population tested with cocoa.

FIG. 2D illustrates a distribution of percentage of female IBS subjects exceeding the 90^(th) and 95^(th) percentile tested with cocoa.

FIG. 3A illustrates ELISA signal score of male IBS patients and control tested with rye.

FIG. 3B illustrates a distribution of percentage of male IBS subjects exceeding the 90^(th) and 95^(th) percentile tested with rye.

FIG. 3C illustrates a signal distribution in women along with the 95^(th) percentile cutoff as determined from the female control population tested with rye.

FIG. 3D illustrates a distribution of percentage of female IBS subjects exceeding the 90^(th) and 95^(th) percentile tested with rye.

FIG. 4A illustrates ELISA signal score of male MS patients and control tested with black tea.

FIG. 4B illustrates a distribution of percentage of male IBS subjects exceeding the 90^(th) and 95^(th) percentile tested with black tea.

FIG. 4C illustrates a signal distribution in women along with the 95^(th) percentile cutoff as determined from the female control population tested with black tea.

FIG. 4D illustrates a distribution of percentage of female IBS subjects exceeding the 90^(th) and 95^(th) percentile tested with black tea.

FIGS. 5A-5B illustrate distributions of IBS subjects by number of foods that were identified as trigger foods at the 90^(th) percentile and 95^(th) percentile.

Table 5 shows raw data of IBS patients and control with number of positive results based on the 90^(th) percentile.

Table 6 shows statistical data summarizing the raw data of IBS patient populations shown in Table 5.

Table 7 shows statistical data summarizing the raw data of control populations shown in Table 5.

Table 8 shows statistical data summarizing the raw data of IBS patient populations shown in Table 5 transformed by logarithmic transformation.

Table 9 shows statistical data summarizing the raw data of control populations shown in Table 5 transformed by logarithmic transformation.

Table 10 shows statistical data of an independent T-test to compare the geometric mean number of positive foods between the IBS and non-IBS samples.

Table 11 shows statistical data of a Mann-Whitney test to compare the geometric mean number of positive foods between the IBS and non-IBS samples.

FIG. 6A illustrates a box and whisker plot of data shown in Table 5.

FIG. 6B illustrates a notched box and whisker plot of data shown in Table 5.

FIG. 7 illustrates the ROC curve corresponding to the statistical data shown in Table 12.

Table 12 shows statistical data of a Receiver Operating Characteristic (ROC) curve analysis of data shown in Tables 5-11.

Table 13 shows statistical data of performance metrics in predicting IBS status among female patients from number of positive foods.

Table 14 shows statistical data of performance metrics in predicting MS status among male patients from number of positive foods.

DETAILED DESCRIPTION

The inventors have discovered that food preparations used in food tests to identify trigger foods in patients diagnosed with or suspected to have IBS are not equally well predictive and/or associated with IBS/IBS symptoms. Indeed, various experiments have revealed that among a wide variety of food items certain food items are highly predictive/associated with IBS whereas others have no statistically significant association with IBS.

Even more unexpectedly, the inventors discovered that in addition to the high variability of food items, gender variability with respect to response in a test plays a substantial role in the determination of association or a food item with IBS. Consequently, based on the inventors' findings and further contemplations, test kits and methods are now presented with substantially higher predictive power in the choice of food items that could be eliminated for reduction of MS signs and symptoms.

The following discussion provides many example embodiments of the inventive subject matter. Although each embodiment represents a single combination of inventive elements, the inventive subject matter is considered to include all possible combinations of the disclosed elements. Thus if one embodiment comprises elements A, B, and C, and a second embodiment comprises elements B and D, then the inventive subject matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly disclosed.

In some embodiments, the numbers expressing quantities or ranges, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term “about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements. Unless the context dictates the contrary, all ranges set forth herein should be interpreted as being inclusive of their endpoints and open-ended ranges should be interpreted to include only commercially practical values. Similarly, all lists of values should be considered as inclusive of intermediate values unless the context indicates the contrary.

As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.

All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.

Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

In one especially preferred aspect, the inventors therefore contemplate a test kit or test panel that is suitable for testing food intolerance in patients where the patient is diagnosed with or suspected to have irritable bowel syndrome. Most preferably, such test kit or panel will include a plurality of distinct food preparations (e.g., raw or processed extract, preferably aqueous extract with optional co-solvent, which may or may not be filtered) that are coupled to individually addressable respective solid carriers (e.g., in a form of an array or a micro well plate), wherein the distinct food preparations have an average discriminatory p-value of ≤0.07 as determined by raw p-value or an average discriminatory p-value of ≤0.10 as determined by FDR multiplicity adjusted p-value. As used herein, processed extracts includes food extracts made of food items that are mechanically or chemically modified (e.g., minced, heated, boiled, fermented, smoked, etc.).

In some embodiments, the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term “about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements. Moreover, and unless the context dictates the contrary, all ranges set forth herein should be interpreted as being inclusive of their endpoints and open-ended ranges should be interpreted to include only commercially practical values. Similarly, all lists of values should be considered as inclusive of intermediate values unless the context indicates the contrary.

While not limiting to the inventive subject matter, food preparations will typically be drawn from foods generally known or suspected to trigger signs or symptoms of IBS. Particularly suitable food preparations may be identified by the experimental procedures outlined below. Thus, it should be appreciated that the food items need not be limited to the items described herein, but that all items are contemplated that can be identified by the methods presented herein. Therefore, exemplary food preparations include at least two, at least four, at least eight, or at least 12 food preparations prepared from cocoa, tea (e.g. green, black, etc.), oat, cabbage, cow milk, onion (e.g. yellow, white, maui, etc.), honey, rye, corn, yeast, wheat (e.g. red, white, etc.), soybean, egg, tuna, lemon, pineapple, cucumber, orange, halibut, walnut, grapefruit, cane sugar, chicken, blueberry, or shrimp (e.g. US Gulf white, Thai, Tiger, etc.). Additionally contemplated food preparations are prepared from Crab (e.g. Dungeness, Blue, Alaskan King, etc.), Barley, Strawberry, Pork, Rice (e.g. Brown, White, etc.), Beef, Cashew, Codfish, Potato, White Sesame, Broccoli, Almond, Turkey, Scallop, and/or Salmon. Still further especially contemplated food items and food additives from which food preparations can be prepared are listed in Table 1.

Using bodily fluids from patients diagnosed with or suspected to have irritable bowel syndrome and healthy control group individuals (i.e., those not diagnosed with or not suspected to have irritable bowel syndrome), numerous additional food items may be identified. Preferably, such identified food items will have high discriminatory power and as such have a p-value of ≤0.15, more preferably ≤0.10, and most preferably ≤0.05 as determined by raw p-value, and/or a p-value of ≤0.10, more preferably ≤0.08, and most preferably ≤0.07 as determined by False Discovery Rate (FDR) multiplicity adjusted p-value.

Therefore, where a panel has multiple food preparations, it is contemplated that the plurality of distinct food preparations has an average discriminatory p-value of ≤0.05 as determined by raw p-value or an average discriminatory p-value of ≤0.08 as determined by FDR multiplicity adjusted p-value, or even more preferably an average discriminatory p-value of ≤0.025 as determined by raw p-value or an average discriminatory p-value of ≤0.07 as determined by FDR multiplicity adjusted p-value. In further preferred aspects, it should be appreciated that the FDR multiplicity adjusted p-value may be adjusted for at least one of age and gender, and most preferably adjusted for both age and gender. On the other hand, where a test kit or panel is stratified for use with a single gender, it is also contemplated that in a test kit or panel at least 50% (and more typically 70% or all) of the plurality of distinct food preparations, when adjusted for a single gender, have an average discriminatory p-value of ≤0.07 as determined by raw p-value or an average discriminatory p-value of ≤0.10 as determined by FDR multiplicity adjusted p-value. Furthermore, it should be appreciated that other stratifications (e.g., dietary preference, ethnicity, place of residence, genetic predisposition or family history, etc.) are also contemplated, and the PHOSITA will be readily appraised of the appropriate choice of stratification.

The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.

Of course, it should be noted that the particular format of the test kit or panel may vary considerably and contemplated formats include micro well plates, a microfluidic device, dip sticks, membrane-bound arrays, etc. Consequently, the solid carrier to which the food preparations are coupled may include wells of a multiwall plate, a microfluidic device, a (e.g., color-coded or magnetic) bead, or an adsorptive film (e.g., nitrocellulose or micro/nanoporous polymeric film), a chemical sensor, or an electrical sensor, (e.g. a printed copper sensor or microchip). In some embodiments, it is also contemplated that a suitable solid carrier for molecular absorption and signal detection by a light detector (e.g., surface plasmon resonance, etc.) can be used.

Consequently, the inventors also contemplate a method of testing food intolerance in patients that are diagnosed with or suspected to have irritable bowel syndrome. Most typically, such methods will include a step of contacting a food preparation with a bodily fluid (e.g., whole blood, plasma, serum, saliva, or a fecal suspension) of a patient that is diagnosed with or suspected to have irritable bowel syndrome, and wherein the bodily fluid is associated with a gender identification. As noted before, the step of contacting is preferably performed under conditions that allow IgG (or IgE or IgA or IgM) from the bodily fluid to bind to at least one component of the food preparation, and the IgG bound to the component(s) of the food preparation are then quantified/measured to obtain a signal. Most preferably, the signal is then compared against a gender-stratified reference value (e.g., at least a 90th percentile value) for the food preparation using the gender identification to obtain a result, which is then used to update or generate a report. Preferably, the report can be generated as an aggregate result of individual assay results.

Most commonly, such methods will not be limited to a single food preparation, but will employ multiple different food preparations. As noted before, suitable food preparations can be identified using various methods as described below, however, especially preferred food preparations include cocoa, tea (e.g. green, black, etc.), oat, cabbage, cow milk, onion (e.g. yellow, white, maui, etc.), honey, rye, corn, yeast, wheat (e.g. red, white, etc.), soybean, egg, tuna, lemon, pineapple, cucumber, orange, halibut, walnut, grapefruit, cane sugar, chicken, blueberry, or shrimp (e.g. US Gulf white, Thai, Tiger, etc.). Additionally contemplated food preparations are prepared from Crab (e.g. Dungeness, Blue, Alaskan King, etc.), Barley, Strawberry, Pork, Rice (e.g. Brown, White, etc.), Beef, Cashew, Codfish, Potato, White Sesame, Broccoli, Almond, Turkey, Scallop, and/or Salmon, and/or items of Table 1. As also noted above, it is generally preferred that at least some, or all of the different food preparations have an average discriminatory p-value of ≤0.07 (or ≤0.05, or ≤0.025) as determined by raw p-value, and/or or an average discriminatory p-value of ≤0.10 (or ≤0.08, or ≤0.07) as determined by FDR multiplicity adjusted p-value.

While it is preferred that food preparations are prepared from a single food items as crude extracts, or crude filtered extracts, it is contemplated that food preparations can be prepared from mixtures of a plurality of food items (e.g. a mixture of citrus comprising lemon, orange and lime, a mixture of crabs comprising blue crab, king crab and Dungeness crab, a mixture of wheat comprising a white wheat and red wheat, a mixture of shrimp comprising US Gulf white, Thai and Tiger shrimps, etc). In some embodiments, it is also contemplated that food preparations can be prepared from purified food antigens or recombinant food antigens.

As it is generally preferred that the food preparation is immobilized on a solid surface (typically in an addressable manner), it is contemplated that the step of measuring the IgG or other type of antibody bound to the component of the food preparation is performed via an immunoassay test (e.g., ELISA test, antibody capture enzyme immunoassay, other types of antibody capture assays, etc.)

Viewed from a different perspective, the inventors also contemplate a method of generating a test for food intolerance in patients diagnosed with or suspected to have irritable bowel syndrome. Because the test is applied to patients already diagnosed with or suspected to have irritable bowel syndrome, the authors do not contemplate that the method has a primary diagnostic purpose for IBS. Instead, the method is for identifying triggering food items among already diagnosed or suspected IBS patients. Such test will typically include a step of obtaining one or more test results (e.g., ELISA, antibody capture enzyme immunoassay) for various distinct food preparations, wherein the test results are based on bodily fluids (e.g., blood saliva, fecal suspension) of patients diagnosed with or suspected to have irritable bowel syndrome and bodily fluids of a control group not diagnosed with or not suspected to have irritable bowel syndrome. Most preferably, the test results are then stratified by gender for each of the distinct food preparations, a different cutoff value for male and female patients for each of the distinct food preparations (e.g., cutoff value for male and female patients has a difference of at least 10% (abs)) is assigned for a predetermined percentile rank (e.g., 90th or 95th percentile).

As noted earlier, and while not limiting to the inventive subject matter, it is contemplated that the distinct food preparations include at least two (or six, or ten, or 15) food preparations prepared from food items selected from the group consisting of cocoa, tea (e.g. green, black, etc.), oat, cabbage, cow milk, onion (e.g. yellow, white, maui, etc.), honey, rye, corn, yeast, wheat (e.g. red, white, etc.), soybean, egg, tuna, lemon, pineapple, cucumber, orange, halibut, walnut, grapefruit, cane sugar, chicken, blueberry, or shrimp (e.g. US Gulf white, Thai, Tiger, etc.). Additionally contemplated food preparations are prepared from Crab (e.g. Dungeness, Blue, Alaskan King, etc.), Barley, Strawberry, Pork, Rice (e.g. Brown, White, etc.), Beef, Cashew, Codfish, Potato, White Sesame, Broccoli, Almond, Turkey, Scallop, and/or Salmon, and/or items of Table 1. On the other hand, where new food items are tested, it should be appreciated that the distinct food preparations include a food preparation prepared from a food items other than cocoa, tea (e.g. green, black, etc.), oat, cabbage, cow milk, onion (e.g. yellow, white, maui, etc.), honey, rye, corn, yeast, wheat (e.g. red, white, etc.), soybean, egg, tuna, lemon, pineapple, cucumber, orange, halibut, walnut, grapefruit, cane sugar, chicken, blueberry, or shrimp (e.g. US Gulf white, Thai, Tiger, etc.). Regardless of the particular choice of food items, it is generally preferred however, that the distinct food preparations have an average discriminatory p-value of ≤0.07 (or ≤0.05, or ≤0.025) as determined by raw p-value or an average discriminatory p-value of ≤0.10 (or ≤0.08, or ≤0.07) as determined by FDR multiplicity adjusted p-value. Exemplary aspects and protocols, and considerations are provided in the experimental description below.

Thus, it should be appreciated that by having a high-confidence test system as described herein, the rate of false-positive and false negatives can be significantly reduced, and especially where the test systems and methods are gender stratified or adjusted for gender differences as shown below. Such advantages have heretofore not been realized and it is expected that the systems and methods presented herein will substantially increase the predictive power of food sensitivity tests for patients diagnosed with or suspected to have IBS.

Experiments

General Protocol for Food Preparation Generation:

Commercially available food extracts (available from Biomerica Inc., 17571 Von Karman Ave, Irvine, Calif. 92614) prepared from the edible portion of the respective raw foods were used to prepare ELISA plates following the manufacturer's instructions.

For some food extracts, the inventors found that food extracts prepared with specific procedures to generate food extracts provides more superior results in detecting elevated IgG reactivity in IBS patients compared to commercially available food extracts. For example, for grains and nuts, a three-step procedure of generating food extracts is preferred. The first step is a defatting step. In this step, lipids from grains and nuts are extracted by contacting the flour of grains and nuts with a non-polar solvent and collecting residue. Then, the defatted grain or nut flour are extracted by contacting the flour with elevated pH to obtain a mixture and removing the solid from the mixture to obtain the liquid extract. Once the liquid extract is generated, the liquid extract is stabilized by adding an aqueous formulation. In a preferred embodiment, the aqueous formulation includes a sugar alcohol, a metal chelating agent, protease inhibitor, mineral salt, and buffer component 20-50 mM of buffer from 4-9 pH. This formulation allowed for long term storage at −70° C. and multiple freeze-thaws without a loss of activity.

For another example, for meats and fish, a two step procedure of generating food extract is preferred. The first step is an extraction step. In this step, extracts from raw, uncooked meats or fish are generated by emulsifying the raw, uncooked meats or fish in an aqueous buffer formulation in a high impact pressure processor. Then, solid materials are removed to obtain liquid extract. Once the liquid extract is generated, the liquid extract is stabilized by adding an aqueous formulation. In a preferred embodiment, the aqueous formulation includes a sugar alcohol, a metal chelating agent, protease inhibitor, mineral salt, and buffer component 20-50 mM of buffer from 4-9 pH. This formulation allowed for long term storage at −70° C. and multiple freeze-thaws without a loss of activity.

For still another example, for fruits and vegetables, a two step procedure of generating food extract is preferred. The first step is an extraction step. In this step, liquid extracts from fruits or vegetables are generated using an extractor (e.g., masticating juicer, etc) to pulverize foods and extract juice. Then, solid materials are removed to obtain liquid extract. Once the liquid extract is generated, the liquid extract is stabilized by adding an aqueous formulation. In a preferred embodiment, the aqueous formulation includes a sugar alcohol, a metal chelating agent, protease inhibitor, mineral salt, and buffer component 20-50 mM of buffer from 4-9 pH. This formulation allowed for long term storage at −70° C. and multiple freeze-thaws without a loss of activity.

Blocking of ELISA Plates:

To optimize signal to noise, plates were blocked with a proprietary blocking buffer. In a preferred embodiment, the blocking buffer includes 20-50 mM of buffer from 4-9 pH, a protein of animal origin and a short chain alcohol. Other blocking buffers, including several commercial preparations, were attempted but could not provide adequate signal to noise and low assay variability required.

ELISA Preparation and Sample Testing:

Food antigen preparations were immobilized onto respective microtiter wells following the manufacturer's instructions. For the assays, the food antigens were allowed to react with antibodies present in the patients' serum, and excess serum proteins were removed by a wash step. For detection of IgG antibody binding, enzyme labeled anti-IgG antibody conjugate was allowed to react with antigen-antibody complex. A color was developed by the addition of a substrate that reacts with the coupled enzyme. The color intensity was measured and is directly proportional to the concentration of IgG antibody specific to a particular food antigen.

Methodology to Determine Ranked Food List in Order of Ability of ELISA Signals to Distinguish IBS from Control Subjects:

Out of an initial selection (e.g., 100 food items, or 150 food items, or even more), samples can be eliminated prior to analysis due to low consumption in an intended population. In addition, specific food items can be used as being representative of the a larger more generic food group, especially where prior testing has established a correlation among different species within a generic group (most preferably in both genders, but also suitable for correlation for a single gender). For example, Thailand Shrimp could be dropped in favor of U.S. Gulf White Shrimp as representative of the “shrimp” food group, or King Crab could be dropped in favor of Dungeness Crab as representative of the “crab” food group In further preferred aspects, the final list foods is shorter than 50 food items, and more preferably equal or less than of 40 food items.

Since the foods ultimately selected for the food intolerance panel will not be specific for a particular gender, a gender-neutral food list was necessary. Since the observed sample was imbalanced by gender (e.g., Controls: 22% female, IBS: 64% female), differences in ELISA signal magnitude strictly due to gender were removed by modeling signal scores against gender using a two-sample t-test and storing the residuals for further analysis. For each of the tested foods, residual signal scores were compared between IBS and controls using a permutation test on a two-sample t-test with 50,000 resamplings. The Satterthwaite approximation was used for the denominator degrees of freedom to account for lack of homogeneity of variances, and the 2-tailed permuted p-value represented the raw p-value for each food. False Discovery Rates (FDR) among the comparisons, were adjusted by any, acceptable statistical procedures (e.g., Benjamini-Hochberg, Family-wise Error Rate (FWER), Per Comparison Error Rate (PCER), etc.).

Foods were then ranked according to their 2-tailed FDR multiplicity-adjusted p-values. Foods with adjusted p-values equal to or lower than the desired FDR threshold were deemed to have significantly higher signal scores among IBS than control subjects and therefore deemed candidates for inclusion into a food intolerance panel. A typical result that is representative of the outcome of the statistical procedure is provided in Table 2. Here the ranking of foods is according to 2-tailed permutation T-test p-values with FDR adjustment.

Notably, the inventors discovered that even for the same food preparation tested, the ELISA score for at least several food items varied dramatically, and exemplary raw data are provided in Table 3. As will be readily appreciated, data unstratified by gender will therefore lose significant explanatory power where the same cutoff value is applied to raw data for male and female data. To overcome such disadvantage, the inventors stratified the data by gender as described below.

Statistical Method for Cutpoint Selection for Each Food:

The determination of what ELISA signal scores would constitute a “positive” response was made by summarizing the distribution of signal scores among the Control subjects. For each food, IBS subjects who had have observed scores greater than or equal to selected quantiles of the Control subject distribution were deemed “positive”. To attenuate the influence of any one subject on cutpoint determination, each food-specific and gender-specific dataset was bootstrap resampled 1000 times. Within each bootstrap replicate, the 90th and 95th percentiles of the Control signal scores were determined. Each IBS subject in the bootstrap sample was compared to the 90th and 95% percentiles to determine whether he/she had a “positive” response. The final 90th and 95th percentile-based cutpoints for each food and gender were computed as the average 90th and 95th percentiles across the 1000 samples. The number of foods for which each IBS subject was rated as “positive” was computed by pooling data across foods: Using such method, the inventors were now able to identify cutoff values for a predetermined percentile rank that in most cases was substantially different as can be taken from Table 4.

Typical examples for the gender difference in IgG response in blood with respect to wheat is shown in FIGS. 1A-1D, where FIG. 1A shows the signal distribution in men along with the 95^(th) percentile cutoff as determined from the male control population. FIG. 1B shows the distribution of percentage of male IBS subjects exceeding the 90^(th) and 95^(th) percentile, while FIG. 1C shows the signal distribution in women along with the 95^(th) percentile cutoff as determined from the female control population. FIG. 1D shows the distribution of percentage of female IBS subjects exceeding the 90^(th) and 95^(th) percentile. In the same fashion, FIGS. 2A-2D exemplarily depict the differential response to cocoa, FIGS. 3A-3D exemplarily depict the differential response to rye, and FIGS. 4A-4D exemplarily depict the differential response to black tea. FIGS. 5A-5B show the distribution of IBS subjects by number of foods that were identified as trigger foods at the 90^(th) percentile (5A) and 95^(th) percentile (5B). Inventors contemplate that regardless of the particular food items, male and female responses were notably distinct.

It should be noted that nothing in the art have provided any predictable food groups related to IBS that is gender-stratified. Thus, a discovery of food items that show distinct responses by gender is a surprising result, which could not be obviously expected in view of all previously available arts. In other words, selection of food items based on gender stratification provides an unexpected technical effect such that statistical significances for particular food items as triggering food among male or female IBS patients have been significantly improved.

Normalization of IgG Response Data:

While the raw data of the patient's IgG response results can be use to compare strength of response among given foods, it is also contemplated that the IgG response results of a patient are normalized and indexed to generate unit-less numbers for comparison of relative strength of response to a given food. For example, one or more of a patient's food specific IgG results (e.g., IgG specific to Dungeness crab and IgG specific to egg) can be normalized to the patient's total IgG. The normalized value of the patient's IgG specific to Dungeness crab can be 0.1 and the normalized value of the patient's IgG specific to egg can be 0.3. In this scenario, the relative strength of the patient's response to egg is three times higher compared to Dungeness crab. Then, the patient's sensitivity to egg and Dungeness crab can be indexed as such.

In other examples, one or more of a patient's food specific IgG results (e.g., IgG specific to shrimp and IgG specific to pork) can be normalized to the global mean of that patient's food specific IgG results. The global means of the patient's food specific IgG can be measured by total amount of the patient's food specific IgG. In this scenario, the patient's specific IgG to shrimp can be normalized to the mean of patient's total food specific IgG (e.g., mean of IgG levels to shrimp, pork, Dungeness crab, chicken, peas, etc.). However, it is also contemplated that the global means of the patient's food specific IgG can be measured by the patient's IgG levels to a specific type of food via multiple tests. If the patient have been tested for his sensitivity to shrimp five times and to pork seven times previously, the patient's new IgG values to shrimp or to pork are normalized to the mean of five-times test results to shrimp or the mean of seven-times test results to pork. The normalized value of the patient's IgG specific to shrimp can be 6.0 and the normalized value of the patient's IgG specific to pork can be 1.0. In this scenario, the patient has six times higher sensitivity to shrimp at this time compared to his average sensitivity to shrimp, but substantially similar sensitivity to pork. Then, the patient's sensitivity to shrimp and pork can be indexed based on such comparison.

Methodology to Determine the Subset of IBS Patients with Food Sensitivities that Underlie IBS:

While it is suspected that food sensitivities plays a substantial role in signs and symptoms of IBS, some IBS patients may not have food sensitivities that underlie IBS. Those patients would not be benefit from dietary intervention to treat signs and symptoms of IBS. To determine the subset of such patients, body fluid samples of IBS patients and non-IBS patients can be tested with ELISA test using test devices with 24 food samples.

Table 5 provides exemplary raw data. As should be readily appreciated, data indicates number of positive results out of 24 sample foods based on 90^(th) percentile value. From the raw data shown in Table 5, average and standard deviation of the number of positive foods was computed for IBS and non-IBS patients. Additionally, the number and percentage of patients with zero positive foods was calculated for both IBS and non-IBS. The number and percentage of patients with zero positive foods is about 5 fold lower in the IBS population than in the non-IBS population (6% vs. 38%, respectively). Thus, it can be easily appreciated that the IBS patient having sensitivity to zero positive foods is unlikely to have food sensitivities underlying their signs and symptoms of IBS.

Table 6 and Table 7 show exemplary statistical data summarizing the raw data of two patient populations shown in Table 5. The statistical data includes normality, arithmetic mean, median, percentiles and 95% confidence interval (CI) for the mean and median representing number of positive foods in the MS population and the non-IBS population.

Table 8 and Table 9 show another exemplary statistical data summarizing the raw data of two patient populations shown in Table 5. In Tables 8 and 9, the raw data was transformed by logarithmic transformation to improve the data interpretation.

Table 10 and Table 11 show exemplary statistical data of an independent T-test (Table. 10, logarithmically transformed data) and a Mann-Whitney test (Table 11) to compare the geometric mean number of positive foods between the IBS and non-IBS samples. The data shown in Table 10 and Table 11 indicates statistically significant differences in the geometric mean of positive number of foods between the IBS population and the non-IBS population. In both statistical tests, it is shown that the number of positive responses with 24 food samples is significantly higher in the IBS population than in the non-IBS population with an average discriminatory p-value of ≤0.001. These statistical data is also illustrated as a box and whisker plot in FIG. 6A, and a notched box and whisker plot in FIG. 6B.

Table 12 shows exemplary statistical data of a Receiver Operating Characteristic (ROC) curve analysis of data shown in Tables 5-11 to determine the diagnostic power of the test used in Table 5 at discriminating IBS from non-IBS subjects. When a cutoff criterion of more than 2 positive foods is used, the test yields a data with 72.4% sensitivity and 72.2% specificity, with an area under the curve (AUROC) of 0.771. The p-value for the ROC is significant at a p-value of <0.0001. FIG. 7 illustrates the ROC curve corresponding to the statistical data shown in Table 12. Because the statistical difference between the IBS population and the non-IBS population is significant when the test results are cut off to positive number of 2, the number of foods that a patient tests positive could be used as a confirmation of the primary clinical diagnosis IBS, and whether it is likely that food sensitivities underlies on the patient's signs and symptoms of IBS. Therefore, the above test can be used as another ‘rule in’ test to add to currently available clinical criteria for diagnosis for IBS.

Method for Determining Distribution of Per-Person Number of Foods Declared “Positive”:

To determine the distribution of number of “positive” foods per son and measure the diagnostic performance, the analysis was performed with 24 food items from the Table 1, which shows most positive responses to IBS patients. The 24 food items includes Cocoa, Black Tea, Oat, Cabbage, Cow's Milk, Yellow Onion, Honey, Rye, Corn, Yeast White Wheat, Soybean, Egg, Tuna, Lemon, Pineapple, Cucumber, Orange, Halibut, Walnut, Grapefruit, Cane Sugar, Chicken, US Gulf White Shrimp. To attenuate the influence of any one subject on this analysis, each food-specific and gender-specific dataset was bootstrap resampled 1000 times. Then, for each food item in the bootstrap sample, sex-specific cutpoint was determined using the 90th and 95th percentiles of the control population. Once the sex-specific cutpoints were determined, the sex-specific cutpoints was compared with the observed ELISA signal scores for both control and IBS subjects. In this comparison, if the observed signal is equal or more than the cutpoint value, then it is determined “positive” food, and if the observed signal is less than the cutpoint value, then it is determined “negative” food.

Once all food items were determined either positive or negative, the results of the 48 (24 foods×2 cutpoints) calls for each subject were saved within each bootstrap replicate. Then, for each subject, 24 calls were summed using 90^(th) percentile as cutpoint to get “Number of Positive Foods (90^(th)),” and the rest of 24 calls were summed using 95^(th) percentile to get “Number of Positive Foods (95^(th))” Then, within each replicate, “Number of Positive Foods (90^(th))” and “Number of Positive Foods (95^(th))” were summarized across subjects to get descriptive statistics for each replicate as follows: 1) overall means equals to the mean of means, 2) overall standard deviation equals to the mean of standard deviations, 3) overall medial equals to the mean of medians, 4) overall minimum equals to the minimum of minimums, and 5) overall maximum equals to maximum of maximum. In this analysis, to avoid non-integer “Number of Positive Foods” when computing frequency distribution and histogram, the authors pretended that the 1000 repetitions of the same original dataset were actually 999 sets of new subjects of the same size added to the original sample. Once the summarization of data is done, frequency distributions and historgrams were generated for both “Number of Positive Foods (90^(th))” and “Number of Positive Foods (95^(th))” for both genders and for both IBS subjects and control subjects using programs “a_pos_foods.sas, a_pos_foods_by_dx.sas”.

Method for Measuring Diagnostic Performance:

To measure diagnostic performance for each food items for each subject, we used data of “Number of Positive Foods (90^(th))” and “Number of Positive Foods (95^(th))” for each subject within each bootstrap replicate described above. In this analysis, the cutpoint was set to 1. Thus, if a subject has one or more “Number of Positive Foods (90^(th))”, then the subject is called “Has IBS.” If a subject has less than one “Number of Positive Foods (90^(th))”, then the subject is called “Does Not Have IBS.” When all calls were made, the calls were compared with actual diagnosis to determine whether a call was a True Positive (TP), True Negative (TN), False Positive (FP), or False Negative (FN). The comparisons were summarized across subjects to get the performance metrics of sensitivity, specificity, positive predictive value, and negative predictive value for both “Number of Positive Foods (90^(th))” and “Number of Positive Foods (95^(th))” when the cutpoint is set to 1 for each method. Each (sensitivity, 1-specificity) pair becomes a point on the ROC curve for this

-   -   replicate.

To increase the accuracy, the analysis above was repeated by incrementing cutpoint from 2 up to 24, and repeated for each of the 1000 bootstrap replicates. Then the performance metrics across the 1000 bootstrap replicates were summarized by calculating averages using a program “t_pos_foods_by_dx.sas”. The results of diagnostic performance for female and male are shown in Table 13 (female) and Table 14 (male).

Of course, it should be appreciated that certain variations in the food preparations may be made without altering the inventive subject matter presented herein. For example, where the food item was yellow onion, that item should be understood to also include other onion varieties that were demonstrated to have equivalent activity in the tests. Indeed, the inventors have noted that for each tested food preparation, certain other related food preparations also tested in the same or equivalent manner (data not shown). Thus, it should be appreciated that each tested and claimed food preparation will have equivalent related preparations with demonstrated equal or equivalent reactions in the test.

It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refers to at least one of something selected from the group consisting of A, B, C . . . and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.

TABLE 1 Abalone Cured Cheese Onion Walnut, black Adlay Cuttlefish Orange Watermelon Almond Duck Oyster Welch Onion American Cheese Durian Papaya Wheat Apple Eel Paprika Wheat bran Artichoke Egg White (separate) Parsley Yeast (S. cerevisiae) Asparagus Egg Yolk (separate) Peach Yogurt Avocado Egg, white/yolk (comb.) Peanut Baby Bok Choy Eggplant Pear FOOD ADDITIVES Bamboo shoots Garlic Pepper, Black Arabic Gum Banana Ginger Pineapple Carboxymethyl Cellulose Barley, whole grain Gluten-Gliadin Pinto bean Carrageneenan Beef Goat's milk Plum FD&C Blue #1 Beets Grape, white/concord Pork FD&C Red #3 Beta-lactoglobulin Grapefruit Potato FD&C Red #40 Blueberry Grass Carp Rabbit FD&C Yellow #5 Broccoli Green Onion Rice FD&C Yellow #6 Buckwheat Green pea Roquefort cheese Gelatin Butter Green pepper Rye Guar Gum Cabbage Guava Saccharine Maltodextrin Cane sugar Hair Tail Safflower seed Pectin Cantaloupe Hake Salmon Whey Caraway Halibut Sardine Xanthan Gum Carrot Hazelnut Scallop Casein Honey Sesame Cashew Kelp Shark fin Cauliflower Kidney bean Sheep's milk Celery Kiwi Fruit Shrimp Chard Lamb Sole Cheddar Cheese Leek Soybean Chick Peas Lemon Spinach Chicken Lentils Squashes Chili pepper Lettuce, Iceberg Squid Chocolate Lima bean Strawberry Cinnamon Lobster String bean Clam Long.an Sunflower seed Cocoa Bean Mackerel Sweet potato Coconut Malt Swiss cheese Codfish Mango Taro Coffee Marjoram Tea, black Cola nut Millet Tobacco Corn Mung bean Tomato Cottage cheese Mushroom Trout Cow's milk Mustard seed Tuna Crab Oat Turkey Cucumber Olive Vanilla

Ranking of Foods According to 2-Tailed Permutation T-Test p-Values with FDR Adjustment

TABLE 2 FDR Raw Multiplicity-adj Rank Food p-value p-value 1 Cocoa 0.0000 0.0000 2 Black Tea 0.0001 0.0020 3 Oat 0.0002 0.0032 4 Cabbage 0.0004 0.0032 5 Cows Milk 0.0004 0.0032 6 Yellow Onion 0.0006 0.0041 7 Honey 0.0008 0.0044 8 Rye 0.0010 0.0044 9 Corn 0.0010 0.0044 10 Yeast 0.0012 0.0047 11 White Wheat 0.0015 0.0055 12 Soybean 0.0020 0.0066 13 Egg 0.0022 0.0069 14 Tuna 0.0029 0.0084 15 Lemon 0.0036 0.0096 16 Pineapple 0.0045 0.0103 17 Cucumber 0.0046 0.0103 18 Orange 0.0046 0.0103 19 Halibut 0.0057 0.0120 20 Walnut 0.0062 0.0125 21 Grapefruit 0.0085 0.0161 22 Cane Sugar 0.0174 0.0316 23 Chicken 0.0184 0.0321 24 Blueberry 0.0218 0.0363 25 US Gulf White 0.0230 0.0367 Shrimp 26 Dungeness Crab 0.0346 0.0533 27 Barley 0.0440 0.0652 28 Strawberry 0.0555 0.0793 29 Pork 0.0976 0.1312 30 Brown Rice 0.0984 0.1312 31 Beef 0.1067 0.1377 32 Cashew 0.1375 0.1718 33 Codfish 0.1741 0.2111 34 Potato 0.2443 0.2825 35 White Sesame 0.2472 0.2825 36 Broccoli 0.2589 0.2876 37 Almond 0.3174 0.3432 38 Turkey 0.4028 0.4240 39 Scallop 0.7149 0.7332 40 Salmon 0.9352 0.9352

Basic Descriptive Statistics of ELISA Score by Food and Gender Comparing IBS to Control

TABLE 3 ELISA Score Sex Food Diagnosis N Mean SD Min Max F Almond Control 26 0.227 0.124 0.100 0.565 IBS 46 0.358 0.474 0.078 3.065 Diff (1 − 2) — −0.132 0.387 — — Barley Control 26 0.255 0.144 0.093 0.725 IBS 46 0.450 0.361 0.118 1.676 Diff (1 − 2) — −0.195 0.302 — — Beef Control 26 0.170 0.081 0.086 0.439 IBS 45 0.190 0.090 0.072 0.467 Diff (1 − 2) — −0.020 0.087 — — Black Tea Control 26 0.179 0.075 0.069 0.307 IBS 46 0.272 0.086 0.115 0.508 Diff (1 − 2) — −0.093 0.083 — — Blueberry Control 26 0.425 0.190 0.233 1.061 IBS 46 0.480 0.143 0.239 0.867 Diff (1 − 2) — −0.055 0.162 — — Broccoli Control 26 0.220 0.127 0.118 0.620 IBS 46 0.280 0.174 0.106 1.042 Diff (1 − 2) — −0.059 0.159 — — Brown Rice Control 26 0.236 0.118 0.082 0.449 IBS 46 0.253 0.136 0.101 0.690 Diff (1 − 2) — −0.018 0.130 — — Cabbage Control 26 0.285 0.161 0.086 0.642 IBS 46 0.432 0.173 0.132 1.033 Diff (1 − 2) — −0.147 0.169 — — Cane Sugar Control 26 0.377 0.272 0.070 0.835 IBS 46 0.521 0.234 0.107 0.975 Diff (1 − 2) — −0.143 0.248 — — Cashew Control 26 0.249 0.277 0.080 1.528 IBS 46 0.286 0.215 0.081 1.183 Diff (1 − 2) — −0.037 0.239 — — Chicken Control 25 0.123 0.064 0.056 0.314 IBS 46 0.156 0.097 0.062 0.579 Diff (1 − 2) — −0.033 0.087 — — Cocoa Control 26 0.345 0.151 0.142 0.656 IBS 46 0.587 0.349 0.208 2.030 Diff (1 − 2) — −0.242 0.294 — — Codfish Control 26 0.202 0.081 0.083 0.392 IBS 46 0.182 0.180 0.048 1.069 Diff (1 − 2) — 0.020 0.152 — — Corn Control 26 0.416 0.221 0.114 0.923 IBS 47 0.562 0.333 0.146 1.686 Diff (1 − 2) — −0.146 0.298 — — Cow Milk Control 26 0.676 0.538 0.074 2.212 IBS 46 1.191 0.845 0.134 3.035 Diff (1 − 2) — −0.515 0.750 — — Cucumber Control 26 0.168 0.083 0.079 0.317 IBS 46 0.211 0.071 0.088 0.460 Diff (1 − 2) — −0.043 0.075 — — Dungeness Control 26 0.321 0.187 0.107 0.757 Crab IBS 46 0.390 0.226 0.082 1.285 Diff (1 − 2) — −0.068 0.213 — — Egg Control 26 0.336 0.296 0.060 1.119 IBS 46 0.903 0.858 0.115 3.274 Diff (1 − 2) — −0.567 0.710 — — Grapefruit Control 25 0.154 0.088 0.069 0.458 IBS 46 0.203 0.148 0.085 1.014 Diff (1 − 2) — −0.049 0.130 — — Halibut Control 26 0.246 0.125 0.093 0.544 IBS 46 0.348 0.198 0.103 0.941 Diff (1 − 2) — −0.101 0.175 — — Honey Control 26 0.584 0.306 0.152 1.463 IBS 46 0.805 0.364 0.200 1.638 Diff (1 − 2) — −0.220 0.344 — — Lemon Control 26 0.282 0.157 0.080 0.635 IBS 45 0.444 0.297 0.120 1.567 Diff (1 − 2) — −0.162 0.255 — — Oat Control 26 0.282 0.253 0.071 1.116 IBS 47 0.693 0.680 0.086 2.934 Diff (1 − 2) — −0.411 0.567 — — Orange Control 26 0.222 0.119 0.080 0.549 IBS 46 0.313 0.166 0.106 1.044 Diff (1 − 2) — −0.091 0.151 — — Pineapple Control 26 0.924 0.853 0.098 3.467 IBS 47 1.624 1.015 0.206 3.721 Diff (1 − 2) — −0.700 0.961 — — Pork Control 26 0.392 0.266 0.107 1.285 IBS 46 0.466 0.283 0.064 1.248 Diff (1 − 2) — −0.074 0.277 — — Potato Control 26 0.209 0.104 0.075 0.441 IBS 46 0.266 0.089 0.087 0.474 Diff (1 − 2) — −0.057 0.095 — — Rye Control 26 0.138 0.054 0.073 0.299 IBS 47 0.249 0.193 0.080 1.248 Diff (1 − 2) — −0.111 0.159 — — Salmon Control 26 0.230 0.140 0.102 0.684 IBS 46 0.196 0.100 0.058 0.444 Diff (1 − 2) — 0.034 0.116 — — Scallop Control 25 0.283 0.205 0.086 1.025 IBS 46 0.277 0.173 0.102 0.860 Diff (1 − 2) — 0.005 0.185 — — Soybean Control 26 0.508 0.228 0.210 0.849 IBS 46 0.658 0.230 0.252 1.101 Diff (1 − 2) — −0.150 0.229 — — Strawberry Control 26 0.145 0.059 0.060 0.259 IBS 46 0.176 0.056 0.075 0.370 Diff (1 − 2) — −0.031 0.057 — — Tuna Control 26 0.588 0.297 0.202 1.375 IBS 46 0.859 0.431 0.181 1.875 Diff (1 − 2) — −0.271 0.388 — — Turkey Control 26 0.248 0.110 0.072 0.583 IBS 46 0.267 0.110 0.112 0.691 Diff (1 − 2) — −0.019 0.110 — — US Gulf Control 26 0.563 0.325 0.188 1.693 White Shrimp IBS 45 0.834 0.459 0.210 2.135 Diff (1 − 2) — −0.271 0.415 — — Walnut Control 26 0.194 0.070 0.096 0.315 IBS 46 0.273 0.123 0.135 0.944 Diff (1 − 2) — −0.079 0.107 — — White Sesame Control 26 0.705 0.524 0.190 2.663 IBS 46 0.734 0.393 0.126 1.967 Diff (1 − 2) — −0.029 0.444 — — White Wheat Control 26 0.228 0.125 0.106 0.576 IBS 47 0.427 0.355 0.096 1.872 Diff (1 − 2) — −0.198 0.295 — — Yeast Control 25 0.963 0.624 0.157 2.364 IBS 46 1.291 0.844 0.247 3.438 Diff (1 − 2) — −0.327 0.775 — — Yellow Onion Control 26 0.570 0.442 0.105 1.497 IBS 46 0.911 0.439 0.129 1.791 Diff (1 − 2) — −0.341 0.440 — — M Almond Control 89 0.335 0.391 0.077 2.342 IBS 29 0.361 0.341 0.069 1.442 Diff (1 − 2) — −0.026 0.379 — — Barley Control 89 0.419 0.430 0.110 2.242 IBS 29 0.525 0.499 0.092 1.935 Diff (1 − 2) — −0.106 0.448 — — Beef Control 73 0.184 0.127 0.081 0.979 IBS 27 0.222 0.102 0.078 0.555 Diff (1 − 2) — −0.038 0.121 — — Black Tea Control 89 0.209 0.088 0.080 0.522 IBS 29 0.242 0.076 0.118 0.395 Diff (1 − 2) — −0.033 0.086 — — Blueberry Control 89 0.425 0.228 0.216 2.031 IBS 29 0.517 0.207 0.278 1.424 Diff (1 − 2) — −0.092 0.223 — — Broccoli Control 89 0.242 0.204 0.096 1.747 IBS 29 0.263 0.194 0.133 1.116 Diff (1 − 2) — −0.021 0.201 — — Brown Rice Control 89 0.237 0.123 0.081 0.714 IBS 29 0.288 0.122 0.090 0.554 Diff (1 − 2) — −0.051 0.122 — — Cabbage Control 89 0.322 0.173 0.089 0.873 IBS 29 0.409 0.192 0.105 0.878 Diff (1 − 2) — −0.087 0.178 — — Cane Sugar Control 89 0.375 0.255 0.076 1.097 IBS 29 0.446 0.230 0.098 0.804 Diff (1 − 2) — −0.071 0.249 — — Cashew Control 89 0.230 0.157 0.078 1.152 IBS 29 0.291 0.167 0.072 0.686 Diff (1 − 2) — −0.062 0.160 — — Chicken Control 88 0.134 0.069 0.055 0.339 IBS 29 0.172 0.087 0.055 0.385 Diff (1 − 2) — −0.037 0.074 Cocoa Control 89 0.399 0.198 0.158 1.386 IBS 29 0.623 0.294 0.209 1.310 Diff (1 − 2) — −0.224 0.225 — — Codfish Control 89 0.198 0.191 0.072 1.759 IBS 29 0.146 0.053 0.071 0.325 Diff (1 − 2) — 0.053 0.169 — — Corn Control 89 0.414 0.240 0.137 1.185 IBS 29 0.618 0.330 0.183 1.310 Diff (1 − 2) — −0.204 0.265 — — Cow Milk Control 89 0.805 0.621 0.095 2.416 IBS 29 1.309 0.946 0.128 3.525 Diff (1 − 2) — −0.504 0.713 — — Cucumber Control 89 0.167 0.069 0.070 0.343 IBS 29 0.197 0.070 0.079 0.322 Diff (1 − 2) — −0.030 0.069 — — Dungeness Control 89 0.342 0.175 0.104 1.047 Crab IBS 29 0.431 0.249 0.082 0.992 Diff (1 − 2) — −0.089 0.195 — — Egg Control 89 0.407 0.385 0.071 1.799 IBS 29 0.609 0.681 0.094 2.589 Diff (1 − 2) — −0.202 0.474 — Grapefruit Control 88 0.182 0.100 0.063 0.613 IBS 29 0.260 0.185 0.058 0.658 Diff (1 − 2) — −0.079 0.126 — — Halibut Control 89 0.284 0.153 0.105 0.751 IBS 29 0.379 0.267 0.112 1.477 Diff (1 − 2) — −0.095 0.187 — — Honey Control 89 0.644 0.358 0.210 2.059 IBS 29 0.911 0.557 0.227 2.635 Diff (1 − 2) — −0.267 0.415 — — Lemon Control 89 0.358 0.239 0.091 1.254 IBS 29 0.503 0.375 0.082 1.709 Diff (1 − 2) — −0.146 0.278 — Oat Control 89 0.384 0.378 0.066 1.972 IBS 29 0.745 0.666 0.101 2.614 Diff (1 − 2) — −0.361 0.464 Orange Control 89 0.265 0.152 0.079 0.831 IBS 29 0.358 0.252 0.076 1.126 Diff (1 − 2) — −0.093 0.181 — — Pineapple Control 89 1.053 0.915 0.123 3.617 IBS 29 1.354 0.883 0.287 3.126 Diff (1 − 2) — −0.301 0.908 — — Pork Control 89 0.388 0.219 0.110 1.216 IBS 29 0.462 0.244 0.126 1.057 Diff (1 − 2) — −0.075 0.225 — — Potato Control 89 0.249 0.170 0.069 1.408 IBS 29 0.253 0.125 0.100 0.578 Diff (1 − 2) — −0.004 0.161 — — Rye Control 89 0.178 0.100 0.079 0.610 IBS 29 0.237 0.129 0.088 0.651 Diff (1 − 2) — −0.060 0.108 — — Salmon Control 89 0.206 0.132 0.073 0.897 IBS 29 0.229 0.185 0.114 1.065 Diff (1 − 2) — −0.022 0.147 — — Scallop Control 88 0.294 0.187 0.095 0.973 IBS 29 0.320 0.222 0.067 1.161 Diff (1 − 2) — −0.026 0.196 — — Soybean Control 89 0.523 0.292 0.175 1.653 IBS 29 0.715 0.490 0.187 2.583 Diff (1 − 2) — −0.191 0.351 — — Strawberry Control 89 0.151 0.058 0.062 0.311 IBS 29 0.161 0.053 0.077 0.252 Diff (1 − 2) — −0.010 0.057 — — Tuna Control 89 0.725 0.369 0.183 1.752 IBS 29 0.899 0.459 0.213 1.952 Diff (1 − 2) — −0.174 0.393 — — Turkey Control 89 0.252 0.109 0.100 0.711 IBS 29 0.265 0.099 0.109 0.510 Diff (1 − 2) — −0.013 0.107 — — US Gulf Control 89 0.709 0.366 0.226 1.982 White Shrimp IBS 29 0.797 0.422 0.222 1.550 Diff (1 − 2) — −0.089 0.380 — — Walnut Control 89 0.216 0.110 0.095 0.839 IBS 29 0.244 0.085 0.101 0.428 Diff (1 − 2) — −0.029 0.104 — — White Sesame Control 89 0.620 0.375 0.112 1.855 IBS 29 0.744 0.412 0.153 1.970 Diff (1 − 2) — −0.124 0.385 — — White Wheat Control 89 0.290 0.250 0.095 1.681 IBS 29 0.463 0.400 0.130 1.561 Diff (1 − 2) — −0.173 0.293 — — Yeast Control 88 0.940 0.624 0.172 3.157 IBS 29 1.481 0.788 0.416 2.892 Diff (1 − 2) — −0.541 0.668 — — Yellow Onion Control 89 0.558 0.418 0.094 1.672 IBS 29 0.760 0.417 0.098 1.507 Diff (1 − 2) — −0.203 0.418 — —

Upper Quantiles of ELISA Signal Scores Among Control Subjects as Candidates for Test Cutpoints in Determining “Positive” or “Negative” Top 24 Foods Ranked by Descending Order of Discriminatory Ability Using Permutation Test

TABLE Cutpoint Food 90th 95th Ranking Food Sex percentile percentile 1 Cocoa F 0.544 0.587 M 0.581 0.721 2 BlackTea F 0.281 0.296 M 0.337 0.379 3 Oat F 0.647 0.813 M 0.880 1.187 4 Cabbage F 0.507 0.573 M 0.542 0.644 5 CowsMilk F 1.373 1.611 M 1.872 2.133 6 YellowOnion F 1.109 1.214 M 1.142 1.328 7 Honey F 1.022 1.189 M 1.111 1.422 8 Rye F 0.209 0.237 M 0.313 0.400 9 Corn F 0.755 0.835 M 0.774 0.904 10 Yeast F 1.811 2.014 M 1.883 2.102 11 WhiteWheat F 0.409 0.477 M 0.492 0.704 12 Soybean F 0.778 0.806 M 0.891 1.076 13 Egg F 0.794 0.932 M 0.988 1.270 14 Tuna F 1.054 1.208 M 1.276 1.472 15 Lemon F 0.533 0.585 M 0.705 0.885 16 Pineapple F 2.139 2.646 M 2.651 3.030 17 Cucumber F 0.289 0.305 M 0.265 0.301 18 Orange F 0.389 0.456 M 0.483 0.589 19 Halibut F 0.451 0.497 M 0.506 0.600 20 Walnut F 0.288 0.297 M 0.319 0.387 21 Grapefruit F 0.267 0.333 M 0.328 0.380 22 CaneSugar F 0.739 0.775 M 0.746 0.834 23 Chicken F 0.214 0.249 M 0.250 0.275 24 Blueberry F 0.676 0.807 M 0.630 0.787

TABLE 5 IBS Population Non-IBS Population #of Positive # of Positive Results Results based on 90th based Sample ID Percentile Sample ID on 90th IBS-3 5 BRH-768035 0 IBS-5 0 BRH-768034 1 IBS-11 1 BRH-768033 1 IBS-12 9 BRH-768032 1 IBS-14 0 BRH-768031 1 IBS-18 0 BRH-763484 0 IBS-19 1 BRH-768029 10 IBS-23 11 BRH-768028 8 IBS-24 1 BRH-763510 4 IBS-30 9 BRH-768036 0 IBS-33 8 BRH-768037 1 IBS-35 7 BRH-768038 1 IBS-38 6 BRH-768039 0 IBS-40 3 BRH-768040 0 IBS-42 6 BRH-768041 1 IBS-43 2 BRH-768042 1 BRH-698596 4 BRH-768043 5 BHR-698597 9 BRH-768044 3 BRH-698598 4 BRH-768055 1 BRH-698599 18 BRH-768054 1 BRH-698600 3 BRH-764371 0 BRH-698621 12 BRH-768056 2 BRH-698622 7 BRH-764372 2 BRH-698623 5 BRH-764377 5 BRH-698624 1 BRH-764378 2 BRH-698625 9 BRH-763531 1 BRH-774496 17 BRH-764329 0 BRH-763476 0 BRH-763533 0 BRH-768030 0 BRH-763529 0 IBS-2 1 BRH-763553 12 IBS-4 1 BRH-763528 0 IBS-6 1 BRH-763509 0 IBS-7 9 BRH-763517 2 IBS-8 9 BRH-763500 0 IBS-9 1 BRH-764332 0 IBS-10 14 BRH-764338 1 IBS-13 19 BRH-764337 3 IBS-15 9 BRH-764341 1 IBS-16 1 BRH-764340 1 IBS-17 9 BRH-764342 0 IBS-20 16 BRH-764347 1 IBS-21 23 BRH-764343 5 IBS-22 20 BRH-774498 1 IBS-25 2 BRH-768027 12 IBS-26 16 BRH-768000 1 IBS-27 8 BRH-774499 12 IBS-28 10 BRH-774502 4 IBS-29 8 BRH-774504 8 IBS-31 4 BRH-767999 0 IBS-32 2 BRH-764350 0 IBS-34 0 BRH-763534 0 IBS-36 3 BRH-763506 0 IBS-37 5 BRH-774495 2 IBS-39 19 BRH-764353 0 IBS-41 5 BRH-764355 0 IBS-44 5 BRH-764356 0 BRH-698601 10 BRH-764361 1 BRH-698602 3 BRH-764368 1 BRH-698603 13 BRH-768053 2 BRH-698604 10 BRH-764370 1 BRH-698605 8 BRH-764346 0 BRH-698606 4 BRH-768052 0 BRH-698607 2 BRH-764335 10 BRH-698608 4 BRH-774510 2 BRH-698609 1 BRH-774511 0 BRH-698610 1 BRH-768001 2 BRH-698611 3 BRH-768007 0 BRH-698612 3 BRH-768008 3 BRH-698613 12 BRH-767995 0 BRH-698614 11 BRH-767992 3 BRH-698615 4 BRH-767991 0 BRH-698616 5 BRH-764344 2 BRH-698617 11 BRH-764386 0 BRH-698618 6 BRH-763513 7 BRH-698619 9 BRH-763530 5 BRH-698620 6 BRH-764345 1 No of observation 76 BRH-764336 0 Average # of 6.63 BRH-764352 4 SD 5.54 BRH-764360 0 BRH-764339 4 BRH-763527 17 BRH-764334 1 BRH-764349 0 BRH-764380 0 BRH-764366 0 BRH-763526 19 BRH-764351 2 BRH-763503 3 BRH-764365 3 BRH-764381 0 BRH-763523 0 BRH-774500 3 BRH-774501 1 BRH-774505 6 BRH-774503 2 BRH-774494 0 BRH-774493 0 BRH-774492 1 BRH-774491 0 BRH-764357 2 BRH-764358 0 BRH-768045 0 BRH-768047 1 BRH-768048 8 BRH-768049 12 BRH-768051 0 BRH-768050 5 BRH-774506 11 BRH-774507 0 BRH-774509 0 BRH-774512 0 BRH-774513 4 BRH-774514 1 BRH-764359 3 BRH-763524 1 No of observation 115 Average # of 2.37 SD 3.67

TABLE 6 IBS Variable IBS Sample size 76 Lowest value 0.0000 Highest value 23.0000 Arithmetic mean 6.6316 95% Cl for the mean 5.3651 to 7.8980 Median 5.0000 95% Cl for the median 4.0000 to 8.0000 Variance 30.7158 Standard deviation 5.5422 Relative standard deviation 0.8357 (83.57%) Standard error of the mean 0.6357 Coefficient of Skewness 0.9423 (P = 0.0017 Coefficient of Kurtosis 0.3684 (P = 0.4053 D'Agostino-Pearson test reject Normality (P = 0.0051) for Normal distribution Percentiles 95% Confidence Interval 2.5 0.0000 5 0.0000 0.0000 to 1.0000 10 1.0000 0.0000 to 1.0000 25 2.0000 1.0000 to 3.1043 75 9.0000 8.8957 to 11.6255 90 15.8000 11.0000 to 19.0000 95 18.7000 15.2303 to 22.7004 97.5 19.6000

TABLE 7 Non_IBS Variable Non-IBS Sample size 115 Lowest value 0.0000 Highest value 19.0000 Arithmetic mean 2.3652 95% Cl for the mean 1.6879 to 3.0426 Median 1.0000 95% Cl for the median 1.0000 to 1.0000 Variance 13.4444 Standard deviation 3.6667 Relative standard deviation 1.5502 (155.02%) Standard error of the mean 0.3419 Coefficient of Skewness 2.3537 (P < 0.0001 Coefficient of Kurtosis 5.8546 (P < 0.0001) D'Agostino-Pearson test reject Normality (P < 0.0001 for Normal distribution Percentiles 95% Confidence Interval 2.5 0.0000 5 0.0000 0.0000 to 0.0000 10 0.0000 0.0000 to 0.0000 25 0.0000 0.0000 to 0.0000 75 3.0000 2.0000 to 4.0000 90 8.0000 5.0000 to 11.8759 95 11.7500 8.0000 to 16.1360 97.5 12.0000

TABLE 8 IBS_1 Variable IBS Back-transformed after logarithmic transformation. Sample size 76 Lowest value 0.1000 Highest value 23.0000 Geometric mean 3.7394 95% Cl for the mean 2.7247 to 5.1321 Median 5.0000 95% Cl for the median 4.0000 to 8.0000 Coefficient of Skewness −1.3159 (P = 0.0001 Coefficient of Kurtosis 1.3551 (P = 0.0481) D'Agostino-Pearson test reject Normality (P < 0.0001) for Normal distribution Percentiles 95% Confidence Interval 2.5 0.1000 5 0.1000 0.1000 to 1.0000 10 1.0000 0.1000 to 1.0000 25 2.0000 1.0000 to 3.0914 75 9.0000 8.8901 to 11.6153 90 15.7878 11.0000 to 19.0000 95 18.6943 15.1985 to 22.6812 97.5 19.5938

TABLE 9 non_ IBS_1 Variable non-IBS 1 Back-transformed after logarithmic transformation. Sample size 115 Lowest value 0.1000 Highest value 19.0000 Geometric mean 0.7278 95% Cl for the mean 0.5297 to 1.0001 Median 1.0000 95% Cl for the median 1.0000 to 1.0000 Coefficient of Skewness 0.04343 (P = 0.8428 Coefficient of Kurtosis −1.4006 (P < 0.0001 D'Agostino-Pearson test reject Normality (P < 0.0001) for Normal distribution Percentiles 95% Confidence Interval 2.5 0.1000 5 0.1000 0.1000 to 0.1000 10 0.1000 0.1000 to 0.1000 25 0.1000 0.1000 to 0.1000 75 3.0000 2.0000 to 4.0000 90 8.0000 5.0000 to 11.8711 95 11.7418 8.0000 to 16.0070 97.5 12.0000

TABLE 10 Sample 1 Variable IBS_1 IBS 1 Sample 2 Variable non_IBS_1 non-IBS 1 Back-transformed after logarithmic transformation. Sample 1 Sample 2 Sample size 76 115 Geometric mean 3.7394 0.7278 95% Cl for the mean 2.7247 to 5.1321 0.5297 to 1.0001 Variance of Logs 0.3620 0.5582 F-test for equal variances P = 0.045 T-test (assuming equal variances) Difference on Log-transformed scale Difference −0.7108 Standard Error 0.1025 95% Cl of difference −0.9129 to −0.5087 Test statistic t −6.937 Degrees of Freedom (DF) 189 Two-tailed probability P < 0.0001 Back-transformed results Ratio of geometric means 0.1946 95% Cl of ratio 0.1222 to 0.3100

TABLE 11 Sample 1 Variable IBS_1 IBS-1 Sample 2 Variable non_IBS_1 non-IBS_1 Sample 1 Sample 2 Sample size 76 115 Lowest value 0.1000 0.1000 Highest value 23.0000 19.0000 Median 5.0000 1.0000 95% Cl for the median 4.0000 to 8.0000 1.0000 to 1.0000 lnterquartile range 2.0000 to 9.0000 0.1000 to 3.0000 Mann-Whitney test (independent samples) Average rank of first group 127.1382 Average rank of second group 75.4217 Mann-Whitney U 2003.50 Test statistic Z (corrected for ties) 6.410 Two-tailed probability P < 0.0001

TABLE 12 Variable IBS_Test IBS Test Classification variable Diag Diag Sample size 191 Positive group: Diag = 1 76 Negative group: Diag = 0 115 Disease prevalence (%) unknown Area under the ROC curve (AUC) Area under the ROC curve (AUC) 0.771 Standard Error^(a) 0.0346 95% Confidence interval^(b) 0.705 to 0.828 z statistic 7.829 Significance level P (Area = 0.5) <0.0001 ^(a)DeLong et al., 1988 ^(b)Binomial exact Youden index Youden index J 0.4454 95% Confidence interval^(a) 0.2976 to 0.5542 Associated criterion >2 95% Confidence interval^(a) 0 to 2 ^(a)BC_(a) bootstrap interval (1000 iterations). Summary Table Estimated specificity at fixed sensitivity Sensitivity 95% CI^(a) Criterion Specificity 80.00 57.90 43.98 to 74.43 >0.8364 90.00 41.68 30.23 to 52.71 >0.1455 95.00 38.26 27.83 to 46.09 >0 97.50 38.26 27.83 to 46.09 >0 Sensitivity 80.00 62.89 47.37 to 76.32 >3.2 90.00 39.91 15.58 to 59.59 >7.1667 95.00 15.62  5.83 to 32.66 >11.0625 97.50 13.73  3.95 to 29.91 >11.7812 ^(a)BC_(a) bootstrap interval (1000 iterations). Criterion values and coordinates of the ROC curve [Hide] Criterion Sensitivity 95% CI Specificity 95% CI +LR 95% CI −LR 95% CI ≥0 100.00  95.3-100.0 0.00 0.0-3.2 1.00 1.0-1.0 >0 92.11 83.6-97.0 38.26 29.4-47.8 1.49 1.3-1.7 0.21 0.09-0.5  >1 77.63 66.6-86.4 61.74 52.2-70.6 2.03 1.6-2.6 0.36 0.2-0.6 >2 72.37 60.9-82.0 72.17 63.0-80.1 2.60 1.9-3.6 0.38 0.3-0.6 >3 64.47 52.7-75.1 79.13 70.6-86.1 3.09 2.1-4.6 0.45 0.3-0.6 >4 56.58 44.7-67.9 83.48 75.4-89.7 3.42 2.2-5.4 0.52 0.4-0.7 >5 48.68 37.0-60.4 87.83 80.4-93.2 4.00 2.3-6.9 0.58 0.5-0.7 >6 43.42 32.1-55.3 88.70 81.4-93.8 3.84 2.2-6.8 0.64 0.5-0.8

Performance Metrics in Predicting IBS Status from Number of Positive Foods Using 90th Percentile of ELISA Signal to Determine Positive

TABLE 13 No. of Positive Foods Positive Negative Overall as Sensi- Spe- Predictive Predictive Percent Sex Cutoff tivity cificity Value Value Agreement F 1 0.96 0.35 0.73 0.80 0.74 2 0.82 0.53 0.76 0.64 0.72 3 0.77 0.62 0.78 0.60 0.71 4 0.68 0.67 0.79 0.54 0.68 5 0.63 0.71 0.80 0.50 0.66 6 0.57 0.76 0.81 0.50 0.64 7 0.52 0.81 0.83 0.48 0.62 8 0.46 0.85 0.84 0.46 0.60 9 0.41 0.88 0.85 0.45 0.57 10 0.34 0.88 0.85 0.43 0.54 11 0.28 0.90 0.85 0.42 0.51 12 0.21 0.94 0.85 0.40 0.48 13 0.18 0.94 0.86 0.39 0.46 14 0.15 0.95 0.89 0.39 0.45 15 0.13 1.00 1.00 0.39 0.44 16 0.11 1.00 1.00 0.38 0.43 17 0.10 1.00 1.00 0.38 0.42 18 0.07 1.00 1.00 0.38 0.41 19 0.06 1.00 1.00 0.37 0.40 20 0.04 1.00 1.00 0.37 0.39 21 0.03 1.00 1.00 0.37 0.38 22 0.03 1.00 1.00 0.36 0.37 23 0.00 1.00 1.00 0.36 0.36 24 0.00 1.00 1.00 0.36 0.36

Performance Metrics in Predicting IBS Status from Number of Positive Foods Using 90th Percentile of ELISA Signal to Determine Positive

TABLE 14 No. of Positive Foods Positive Negative Overall as Sensi- Spe- Predictive Predictive Percent Sex Cutoff tivity cificity Value Value Agreement M 1 0.81 0.35 0.29 0.85 0.46 2 0.71 0.57 0.35 0.86 0.61 3 0.67 0.68 0.41 0.86 0.68 4 0.62 0.75 0.45 0.86 0.72 5 0.53 0.80 0.48 0.84 0.74 6 0.47 0.85 0.50 0.83 0.76 7 0.39 0.88 0.50 0.82 0.76 8 0.30 0.90 0.50 0.80 0.75 9 0.25 0.92 0.50 0.79 0.75 10 0.18 0.93 0.43 0.78 0.74 11 0.14 0.94 0.43 0.77 0.74 12 0.11 0.95 0.40 0.77 0.75 13 0.10 0.96 0.43 0.76 0.75 14 0.07 0.97 0.50 0.76 0.75 15 0.06 0.97 0.50 0.76 0.75 16 0.06 0.98 0.50 0.76 0.75 17 0.05 0.98 0.33 0.76 0.75 18 0.00 0.98 0.00 0.75 0.75 19 0.00 0.98 0.00 0.75 0.75 20 0.00 1.00 0.00 0.75 0.75 21 0.00 1.00 0.00 0.75 0.75 22 0.00 1.00 0.00 0.75 0.75 23 0.00 1.00 0.00 0.75 0.75 24 0.00 1.00 0.75 0.75 

1.-98. (canceled)
 99. A method of identifying one or more irritable bowel syndrome (IBS) trigger foods for a patient known to have or suspected of having IBS, the method comprising: contacting a bodily fluid of a patient that is known to have or suspected of having IBS with a plurality of IBS trigger food preparations coupled to a solid carrier, wherein the IBS trigger food preparations are food preparations having a raw p-value of ≤0.07 or a false discovery rate (FDR) multiplicity adjusted p-value of ≤0.10, and wherein the contacting is performed under conditions that allow antibodies from the bodily fluid to bind to at least one component of an IBS trigger food preparation; detecting the antibodies bound to the at least one component to obtain a signal; and identifying one or more IBS trigger foods positive for the patient known to have or suspected of having irritable bowel syndrome using the signal.
 100. The method of claim 99, comprising updating or generating a report for the one or more IBS trigger foods positive for the patient.
 101. The method of claim 99, wherein the detecting comprises measuring or quantifying the antibodies via an immunoassay test.
 102. The method of claim 101, wherein the bodily fluid is serum, and the solid carrier is a microwell plate.
 103. The method of claim 99, wherein the bodily fluid is whole blood, plasma, serum, saliva or a fecal suspension.
 104. The method of claim 99, wherein the IBS trigger food preparation is immobilized on the solid carrier in an individually addressable manner.
 105. The method of claim 99, wherein the solid carrier is selected from the group consisting of an array, a micro well plate, a microfluidic device, a bead, an absorptive film, dip sticks, a chemical sensor, and an electrical sensor.
 106. The method of claim 99, wherein at least two of the IBS trigger food preparations are prepared from food items selected from the group consisting of cocoa, tea, oat, cabbage, cow milk, onion, honey, rye, corn, yeast, wheat, soybean, egg, tuna, lemon, pineapple, cucumber, orange, halibut, walnut, grapefruit, cane sugar, chicken, blueberry, and shrimp.
 107. The method of claim 99, wherein the solid carrier includes at least 10 distinct IBS trigger food preparations.
 108. The method of claim 99, wherein the IBS trigger food preparations are food preparations having a raw p-value of ≤0.05 or a FDR multiplicity adjusted p-value of ≤0.08.
 109. The method of claim 108, wherein the solid carrier includes at least 8 distinct IBS trigger food preparations.
 110. The method of claim 99, wherein at least ten of the IBS trigger food preparations are prepared from food items selected from the group consisting of cocoa, tea, oat, cabbage, cow milk, onion, honey, rye, corn, yeast, wheat, soybean, egg, tuna, lemon, pineapple, cucumber, orange, halibut, walnut, grapefruit, cane sugar, chicken, blueberry, and shrimp.
 111. The method of claim 99, wherein at least fifteen of the IBS trigger food preparations are prepared from food items selected from the group consisting of cocoa, tea, oat, cabbage, cow milk, onion, honey, rye, corn, yeast, wheat, soybean, egg, tuna, lemon, pineapple, cucumber, orange, halibut, walnut, grapefruit, cane sugar, chicken, blueberry, and shrimp.
 112. The method of claim 99, wherein the FDR multiplicity adjusted p-values are adjusted for at least one of age or gender.
 113. The method of claim 99, wherein the plurality of IBS trigger food preparations coupled to a solid carrier are in a test panel, and the totality of food preparations in the test panel has an average raw p-value of ≤0.05 or an average false discover rate (FDR) multiplicity adjusted p-value of ≤0.08.
 114. The method of claim 1, wherein the plurality of IBS trigger food preparations coupled to a solid carrier are in a test panel, and the totality of food preparations in the test panel has an average raw p-value of ≤0.05 or an average false discover rate (FDR) multiplicity adjusted p-value of ≤0.07.
 115. A method of reducing or relieving irritable bowel syndrome (IBS) symptoms in a patient known to have or suspected of having IBS, the method comprising: identifying one or more IBS trigger foods that are positive for a patient known to have or suspected of having irritable bowel syndrome (IBS) according to the method of claim 99; and wherein IBS symptoms are reduced or alleviated in the patient by eliminating one or more of the identified IBS trigger foods from the patient's diet.
 116. A method of generating a test panel for food intolerance in a patient known to have or suspected of having irritable bowel syndrome (IBS), comprising: obtaining test results for a plurality of distinct food preparations, wherein the test results are based on bodily fluids of patients known to have or suspected of having IBS and bodily fluids of a control group not known to have or not suspected to have IBS; selecting a plurality of IBS trigger food preparations, wherein the IBS trigger food preparations are food preparations having a raw p-value of ≤0.07 or a false discovery rate (FDR) multiplicity adjusted p-value of ≤0.10; and generating a test panel comprising the selected IBS trigger food preparations.
 117. The method of claim 116, wherein the selected IBS trigger food preparations include at least two food preparations selected from the group consisting of cocoa, tea, oat, cabbage, cow milk, onion, honey, rye, corn, yeast, wheat, soybean, egg, tuna, lemon, pineapple, cucumber, orange, halibut, walnut, grapefruit, cane sugar, chicken, blueberry, and shrimp.
 118. The method of claim 116, further comprising: stratifying the test results by gender for each of the distinct food preparations; and assigning for a predetermined percentile rank a different cutoff value for male and female patients for each of the distinct food preparations.
 119. The method of claim 118, wherein the predetermined percentile rank is an at least 90^(th) percentile rank.
 120. The method of claim 118, wherein the cutoff value for male and female patients has a difference of at least 10% (abs).
 121. The method of claim 118, further comprising normalizing the test results to the patient's total IgG.
 122. The method of claim 121, further comprising normalizing the test results to the global mean of the patient's food specific IgG results.
 123. The method of claim 118, further comprising identifying a subset of patients, wherein the subset of patient sensitivities to the food preparations underlies IBS by raw p-value or an average discriminatory p-value of ≤0.01.
 124. The method of claim 118, further comprising determining numbers of the food preparations, wherein the numbers of the food preparations can be used to confirm IBS by raw p-value or an average discriminatory p-value of ≤0.01.
 125. A test panel generated according to claim
 116. 126. A test kit comprising the test panel according to claim
 125. 95. Use of claim 74 wherein the plurality of distinct food preparations is processed aqueous extracts.
 96. Use of any one of the claims 74-94 wherein the plurality of distinct food preparations is processed aqueous extracts.
 97. Use of claim 74 wherein the solid carrier is a well of a multiwall plate, a bead, an electrical sensor, a chemical sensor, a microchip, or an adsorptive film.
 98. Use of any one of the claims 74-96 wherein the solid carrier is a well of a multiwall plate, a bead, an electrical sensor, a chemical sensor, a microchip, or an adsorptive film. 